| layout | title | author | minutes |
|---|---|---|---|
topic |
Before we start |
Data Carpentry contributors |
30 |
- Articulating motivations for this lesson
- Set up participants to have a working directory with a
data/folder inside- Introduce R syntax
- Point to relevant information on how to get help, and understand how to ask well formulated questions
R is a free language and environment for statistical computing and graphics. You can search all information about R on the R project website
R system contains two major components:
- Base system – contains the R language software and the high priority add-on packages listed on site.
- User contributed add-on package
Go to the download page, choose the version that suits your computer operating system and follow the instructions.
If you are using a computer with Linux or Mac, open terminal and launch R by typing "R".
If you are using a computer with Windows, launch the application from the desktop icon.
In all cases you will see something like this:
R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i386-apple-darwin9.8.0/i386 (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
>
the symbol > is the prompt, the place where you will communicate with your machine using R. All instructions should be written here either by typing or copy-pasting.
By pressing Enter after an instruction, R will try to execute it, and when ready, show the results and
come back with a new > prompt to wait for new commands.
Instructions are given as commands. A typical command looks like:
> round(3.1415926535897932384626433832795028841971)
[1] 3
>
If R is still waiting for you to enter more data because it isn't complete yet,
the console will show a + prompt. It means that you haven't finished entering
a complete command. This is because you have not 'closed' a parenthesis or
quotation. If this happen press Ctr-c, this should help you out of trouble.
> round(3.1415926535897932384626433832795028841971
+
+
>
To learn in which part of the machine you are use the command getwd()
>getwd()
[1] "/home/username/userfolder"
>
To see the content of the folder use the command dir()
>dir()
[1] "somefile" "someotherfile"
>
You should separate the original data (raw data) from intermediate datasets that
you may create for the need of a particular analysis. For instance, you may want
to create a data/ directory within your working directory that stores the raw
data, and have a data_output/ directory for intermediate datasets and a
figure_output/ directory for the plots you will generate.
To create a directory inside R use the command dir.create
> dir.create("mydata")
> dir()
[1] "mydata" "somefile" "someotherfile"
>
If you want to move in the new created directory use the command setwd:
> getwd()
[1] "/home/username/userfolder"
>
>dir()
[1] "mydata" "somefile" "someotherfile"
>
>setwd("/home/username/userfolder/mydata")
dir()
[1]
>
>setwd("/home/username/userfolder/")
>
>
Type q() to quit
R is a versatile, open source programming/scripting language that's useful both for statistics but also data science. Inspired by the programming language S.
- Open source software under GPL.
- Superior (if not just comparable) to commercial alternatives. R has over 7,000 user contributed packages at this time. It's widely used both in academia and industry.
- Available on all platforms.
- Not just for statistics, but also general purpose programming.
- For people who have experience in programmming: R is both an object-oriented and a so-called functional language
- Large and growing community of peers.
Use # signs to comment. Comment liberally in your R scripts. Anything to the
right of a # is ignored by R.
<- is the assignment operator. It assigns values on the right to objects on
the left. So, after executing x <- 3, the value of x is 3. The arrow can
be read as 3 goes into x.
You can also use = for assignments but not in
all contexts so it is good practice to use <- for assignments. = should only
be used to specify the values of arguments in functions, see below.
An example of assignment is:
a<-4
Functions are "canned scripts" that automate something complicated or convenient
or both. Many functions are predefined, or become available when using the
function library() (more on that later). A function usually gets one or more
inputs called arguments and is written in the form of:
function (arguments)
Functions often (but not always) return a value. A
typical example would be the function sqrt(). The input (the argument) must be
a number, and the return value or output is the square root of that
number.
Executing a function ('running it') is called calling the function. An example of a function call is:
b <- sqrt(a)
Here, the value of a is given to the sqrt() function, the sqrt() function
calculates the square root, and returns the value which is then assigned to
variable b. This function is very simple, because it takes just one
argument.
The return 'value' of a function need not be numerical (like that of sqrt()),
and it also does not need to be a single item: it can be a set of things, or
even a data set. We'll see that when we read data files in to R.
Arguments can be anything, not only numbers or filenames, but also other objects. Exactly what each argument means differs per function, and must be looked up in the documentation (see below). If an argument alters the way the function operates, such as whether to ignore 'bad values', such an argument is sometimes called an option.
Most functions can take several arguments, but many have so-called defaults. If you don't specify such an argument when calling the function, the function itself will fall back on using the default. This is a standard value that the author of the function specified as being "good enough in standard cases". An example would be what symbol to use in a plot. However, if you want something specific, simply change the argument yourself with a value of your choice.
Let's try a function that can take multiple arguments round.
>round(3.14159)
[1] 3
We can see that we get 3. That's because the default is to round
to the nearest whole number. If we want more digits we can see
how to do that by getting information about the round function.
We can use another function args():
>args(round)
function (x, digits = 0)
NULL
>
We can also look at the
help for this function using ?round.
>?round
>
to quit the manual page type q
We see that if we want a different number of digits, we can
type digits=2 or however many we want.
>round(3.14159, digits=2)
If you provide the arguments in the exact same order as they are defined you don't have to name them:
>round(3.14159, 2)
However, it's usually not recommended practice because it's a lot of remembering
to do, and if you share your code with others that include less known functions
it makes your code difficult to read. (It's however OK to not include the names
of the arguments for basic functions like mean, min, etc...)
Another advantage of naming arguments, is that the order doesn't matter. This is useful when there start to be more arguments.
If you need help with a specific function, let's say barplot(), you can type:
?barplot
If you just need to remind yourself of the names of the arguments, you can use:
args(lm)
If the function is part of a package that is installed on your computer but don't remember which one, you can type:
??geom_point
If you are looking for a function to do a particular task, you can use
help.search() (but only looks through the installed packages):
help.search("kruskal")
If you can't find what you are looking for, you can use the rdocumention.org website that search through the help files across all packages available.
Start by googling the error message. However, this doesn't always work very well because often, package developers rely on the error catching provided by R. You end up with general error messages that might not be very helpful to diagnose a problem (e.g. "subscript out of bounds").
However, you should check stackoverflow. Search using the [r] tag. Most
questions have already been answered, but the challenge is to use the right
words in the search to find the answers:
http://stackoverflow.com/questions/tagged/r
The Introduction to R can also be dense for people with little programming experience but it is a good place to understand the underpinnings of the R language.
The R FAQ is dense and technical but it is full of useful information.
The key to get help from someone is for them to grasp your problem rapidly. You should make it as easy as possible to pinpoint where the issue might be.
Try to use the correct words to describe your problem. For instance, a package is not the same thing as a library. Most people will understand what you meant, but others have really strong feelings about the difference in meaning. The key point is that it can make things confusing for people trying to help you. Be as precise as possible when describing your problem
If possible, try to reduce what doesn't work to a simple reproducible
example. If you can reproduce the problem using a very small data.frame
instead of your 50,000 rows and 10,000 columns one, provide the small one with
the description of your problem. When appropriate, try to generalize what you
are doing so even people who are not in your field can understand the question.
To share an object with someone else, if it's relatively small, you can use the
function dput(). It will output R code that can be used to recreate the exact same
object as the one in memory:
dput(head(iris)) # iris is an example data.frame that comes with R
If the object is larger, provide either the raw file (i.e., your CSV file) with
your script up to the point of the error (and after removing everything that is
not relevant to your issue). Alternatively, in particular if your questions is
not related to a data.frame, you can save any R object to a file:
saveRDS(iris, file="/tmp/iris.rds")
The content of this file is however not human readable and cannot be posted directly on stackoverflow. It can however be sent to someone by email who can read it with this command:
some_data <- readRDS(file="~/Downloads/iris.rds")
Last, but certainly not least, always include the output of sessionInfo()
as it provides critical information about your platform, the versions of R and
the packages that you are using, and other information that can be very helpful
to understand your problem.
sessionInfo()
- Your friendly colleagues: if you know someone with more experience than you, they might be able and willing to help you.
- Stackoverlow: if your question hasn't been answered before and is well crafted, chances are you will get an answer in less than 5 min.
- The R-help: it is read by a lot of people (including most of the R core team), a lot of people post to it, but the tone can be pretty dry, and it is not always very welcoming to new users. If your question is valid, you are likely to get an answer very fast but don't expect that it will come with smiley faces. Also, here more than everywhere else, be sure to use correct vocabulary (otherwise you might get an answer pointing to the misuse of your words rather than answering your question). You will also have more success if your question is about a base function rather than a specific package.
- If your question is about a specific package, see if there is a mailing list
for it. Usually it's included in the DESCRIPTION file of the package that can
be accessed using
packageDescription("name-of-package"). You may also want to try to email the author of the package directly. - There are also some topic-specific mailing lists (GIS, phylogenetics, etc...), the complete list is here.
- The Posting Guide for the R mailing lists.
- How to ask for R help useful guidelines